Pandas Profiling Report

Dataset statistics

Number of variables	11
Number of observations	886
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	58.1 KiB
Average record size in memory	67.1 B

Variable types

Numeric	6
Categorical	5

Alerts

`df_index` is highly correlated with `PassengerId`	High correlation
`PassengerId` is highly correlated with `df_index`	High correlation
`Survived` is highly correlated with `Sex_male`	High correlation
`Pclass` is highly correlated with `Fare`	High correlation
`Fare` is highly correlated with `Pclass`	High correlation
`Embarked_Q` is highly correlated with `Embarked_S`	High correlation
`Embarked_S` is highly correlated with `Embarked_Q`	High correlation
`Sex_male` is highly correlated with `Survived`	High correlation
`df_index` is highly correlated with `PassengerId`	High correlation
`PassengerId` is highly correlated with `df_index`	High correlation
`Survived` is highly correlated with `Sex_male`	High correlation
`Pclass` is highly correlated with `Fare`	High correlation
`Fare` is highly correlated with `Pclass`	High correlation
`Embarked_Q` is highly correlated with `Embarked_S`	High correlation
`Embarked_S` is highly correlated with `Embarked_Q`	High correlation
`Sex_male` is highly correlated with `Survived`	High correlation
`df_index` is highly correlated with `PassengerId`	High correlation
`PassengerId` is highly correlated with `df_index`	High correlation
`Survived` is highly correlated with `Sex_male`	High correlation
`Pclass` is highly correlated with `Fare`	High correlation
`Fare` is highly correlated with `Pclass`	High correlation
`Embarked_Q` is highly correlated with `Embarked_S`	High correlation
`Embarked_S` is highly correlated with `Embarked_Q`	High correlation
`Sex_male` is highly correlated with `Survived`	High correlation
`Sex_male` is highly correlated with `Survived`	High correlation
`Survived` is highly correlated with `Sex_male`	High correlation
`df_index` is highly correlated with `PassengerId`	High correlation
`PassengerId` is highly correlated with `df_index`	High correlation
`Survived` is highly correlated with `Sex_male`	High correlation
`Pclass` is highly correlated with `Fare`	High correlation
`SibSp` is highly correlated with `Parch`	High correlation
`Parch` is highly correlated with `SibSp`	High correlation
`Fare` is highly correlated with `Pclass`	High correlation
`Embarked_Q` is highly correlated with `Embarked_S`	High correlation
`Embarked_S` is highly correlated with `Embarked_Q`	High correlation
`Sex_male` is highly correlated with `Survived`	High correlation
`df_index` is uniformly distributed	Uniform
`PassengerId` is uniformly distributed	Uniform
`df_index` has unique values	Unique
`PassengerId` has unique values	Unique
`SibSp` has 603 (68.1%) zeros	Zeros
`Parch` has 674 (76.1%) zeros	Zeros

Reproduction

Analysis started	2022-06-19 05:57:11.989948
Analysis finished	2022-06-19 05:57:28.828808
Duration	16.84 seconds
Software version	pandas-profiling v3.2.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	886
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	444.6173815

Minimum	0
Maximum	890
Zeros	1
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	7.0 KiB

Quantile statistics

Minimum	0
5-th percentile	44.25
Q1	222.25
median	444.5
Q3	665.75
95-th percentile	845.75
Maximum	890
Range	890
Interquartile range (IQR)	443.5

Descriptive statistics

Standard deviation	257.0487858
Coefficient of variation (CV)	0.578134811
Kurtosis	-1.195429246
Mean	444.6173815
Median Absolute Deviation (MAD)	222
Skewness	0.002397173832
Sum	393931
Variance	66074.0783
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1	0.1%
597	1	0.1%
586	1	0.1%
587	1	0.1%
588	1	0.1%
589	1	0.1%
590	1	0.1%
591	1	0.1%
592	1	0.1%
593	1	0.1%
Other values (876)	876	98.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1	0.1%
1	1	0.1%
2	1	0.1%
3	1	0.1%
4	1	0.1%
5	1	0.1%
6	1	0.1%
7	1	0.1%
8	1	0.1%
9	1	0.1%

Value	Count	Frequency (%)
890	1	0.1%
889	1	0.1%
888	1	0.1%
887	1	0.1%
886	1	0.1%
885	1	0.1%
884	1	0.1%
883	1	0.1%
882	1	0.1%
881	1	0.1%

PassengerId
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	886
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	445.6173815

Minimum	1
Maximum	891
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.0 KiB

Quantile statistics

Minimum	1
5-th percentile	45.25
Q1	223.25
median	445.5
Q3	666.75
95-th percentile	846.75
Maximum	891
Range	890
Interquartile range (IQR)	443.5

Descriptive statistics

Standard deviation	257.0487858
Coefficient of variation (CV)	0.5768374316
Kurtosis	-1.195429246
Mean	445.6173815
Median Absolute Deviation (MAD)	222
Skewness	0.002397173832
Sum	394817
Variance	66074.0783
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.1%
598	1	0.1%
587	1	0.1%
588	1	0.1%
589	1	0.1%
590	1	0.1%
591	1	0.1%
592	1	0.1%
593	1	0.1%
594	1	0.1%
Other values (876)	876	98.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.1%
2	1	0.1%
3	1	0.1%
4	1	0.1%
5	1	0.1%
6	1	0.1%
7	1	0.1%
8	1	0.1%
9	1	0.1%
10	1	0.1%

Value	Count	Frequency (%)
891	1	0.1%
890	1	0.1%
889	1	0.1%
888	1	0.1%
887	1	0.1%
886	1	0.1%
885	1	0.1%
884	1	0.1%
883	1	0.1%
882	1	0.1%

Survived
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	7.0 KiB

0	549
1	337

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	886
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	1
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	549	62.0%
1	337	38.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	549	62.0%
1	337	38.0%

Most occurring characters

Value	Count	Frequency (%)
0	549	62.0%
1	337	38.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	886	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	549	62.0%
1	337	38.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	886	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	549	62.0%
1	337	38.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	886	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	549	62.0%
1	337	38.0%

Pclass
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.0 KiB

3	491
1	211
2	184

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	886
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	3
2nd row	1
3rd row	3
4th row	1
5th row	3

Common Values

Value	Count	Frequency (%)
3	491	55.4%
1	211	23.8%
2	184	20.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
3	491	55.4%
1	211	23.8%
2	184	20.8%

Most occurring characters

Value	Count	Frequency (%)
3	491	55.4%
1	211	23.8%
2	184	20.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	886	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	491	55.4%
1	211	23.8%
2	184	20.8%

Most occurring scripts

Value	Count	Frequency (%)
Common	886	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	491	55.4%
1	211	23.8%
2	184	20.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	886	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	491	55.4%
1	211	23.8%
2	184	20.8%

Age
Real number (ℝ)

Distinct	88
Distinct (%)	9.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.16531082 × 10^-16

Minimum	-2.222022385
Maximum	3.901957659
Zeros	0
Zeros (%)	0.0%
Negative	561
Negative (%)	63.3%
Memory size	7.0 KiB

Quantile statistics

Minimum	-2.222022385
5-th percentile	-1.792620416
Q1	-0.5613602919
median	-0.09963774526
Q3	0.4390385591
95-th percentile	1.901159957
Maximum	3.901957659
Range	6.123980043
Interquartile range (IQR)	1.000398851

Descriptive statistics

Standard deviation	1.000564812
Coefficient of variation (CV)	4.620883076 × 10¹⁵
Kurtosis	1.003223995
Mean	2.16531082 × 10^-16
Median Absolute Deviation (MAD)	0.4617225466
Skewness	0.5120820931
Sum	2.011724121 × 10^-13
Variance	1.001129944
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
-0.09963774526	202	22.8%
-0.4074527763	30	3.4%
-0.5613602919	27	3.0%
-0.869175323	26	2.9%
-0.7922215652	25	2.8%
0.05426977028	25	2.8%
-0.6383140497	24	2.7%
-0.3304990186	23	2.6%
0.5159923169	21	2.4%
-0.02268398749	20	2.3%
Other values (78)	463	52.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
-2.222022385	1	0.1%
-2.202783945	1	0.1%
-2.196627645	2	0.2%
-2.190471344	2	0.2%
-2.183545506	1	0.1%
-2.177389205	7	0.8%
-2.100435447	10	1.1%
-2.02348169	6	0.7%
-1.946527932	10	1.1%
-1.869574174	4	0.5%

Value	Count	Frequency (%)
3.901957659	1	0.1%
3.440235112	1	0.1%
3.209373839	2	0.2%
3.17089696	1	0.1%
3.132420081	2	0.2%
2.82460505	1	0.1%
2.747651292	3	0.3%
2.670697534	2	0.2%
2.593743777	2	0.2%
2.516790019	3	0.3%

SibSp
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	7
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.5259593679

Minimum	0
Maximum	8
Zeros	603
Zeros (%)	68.1%
Negative	0
Negative (%)	0.0%
Memory size	7.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	3
Maximum	8
Range	8
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.105151233
Coefficient of variation (CV)	2.101210284
Kurtosis	17.77682411
Mean	0.5259593679
Median Absolute Deviation (MAD)	0
Skewness	3.684609408
Sum	466
Variance	1.221359248
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	603	68.1%
1	209	23.6%
2	28	3.2%
4	18	2.0%
3	16	1.8%
8	7	0.8%
5	5	0.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	603	68.1%
1	209	23.6%
2	28	3.2%
3	16	1.8%
4	18	2.0%
5	5	0.6%
8	7	0.8%

Value	Count	Frequency (%)
8	7	0.8%
5	5	0.6%
4	18	2.0%
3	16	1.8%
2	28	3.2%
1	209	23.6%
0	603	68.1%

Parch
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	7
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3826185102

Minimum	0
Maximum	6
Zeros	674
Zeros (%)	76.1%
Negative	0
Negative (%)	0.0%
Memory size	7.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	6
Range	6
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.807655689
Coefficient of variation (CV)	2.110864131
Kurtosis	9.734674532
Mean	0.3826185102
Median Absolute Deviation (MAD)	0
Skewness	2.744455451
Sum	339
Variance	0.6523077119
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	674	76.1%
1	117	13.2%
2	80	9.0%
5	5	0.6%
3	5	0.6%
4	4	0.5%
6	1	0.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	674	76.1%
1	117	13.2%
2	80	9.0%
3	5	0.6%
4	4	0.5%
5	5	0.6%
6	1	0.1%

Value	Count	Frequency (%)
6	1	0.1%
5	5	0.6%
4	4	0.5%
3	5	0.6%
2	80	9.0%
1	117	13.2%
0	674	76.1%

Fare
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	246
Distinct (%)	27.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.383393024 × 10^-16

Minimum	-0.7407918193
Maximum	5.653180587
Zeros	0
Zeros (%)	0.0%
Negative	659
Negative (%)	74.4%
Memory size	7.0 KiB

Quantile statistics

Minimum	-0.7407918193
5-th percentile	-0.5651399158
Q1	-0.5488316394
median	-0.3893859032
Q3	0.004284656837
95-th percentile	1.954967726
Maximum	5.653180587
Range	6.393972406
Interquartile range (IQR)	0.5531162962

Descriptive statistics

Standard deviation	1.000564812
Coefficient of variation (CV)	7.232686554 × 10¹⁵
Kurtosis	12.13091898
Mean	1.383393024 × 10^-16
Median Absolute Deviation (MAD)	0.1678527159
Skewness	3.204136704
Sum	1.151301277 × 10^-13
Variance	1.001129944
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
-0.545082778	43	4.9%
-0.4247399514	42	4.7%
-0.5488316394	38	4.3%
-0.5523762827	34	3.8%
-0.1086880834	31	3.5%
-0.4855191567	24	2.7%
-0.5481217383	18	2.0%
-0.5517684906	16	1.8%
-0.5650378067	15	1.7%
-0.7407918193	15	1.7%
Other values (236)	610	68.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
-0.7407918193	15	1.7%
-0.6432411947	1	0.1%
-0.6192334086	1	0.1%
-0.5891477019	1	0.1%
-0.5842853655	1	0.1%
-0.5839814695	1	0.1%
-0.5828679944	2	0.2%
-0.5766879648	2	0.2%
-0.5740550096	1	0.1%
-0.5718256284	1	0.1%

Value	Count	Frequency (%)
5.653180587	4	0.5%
5.637985785	2	0.2%
5.276855196	2	0.2%
4.790723662	4	0.5%
4.651033599	1	0.1%
4.401128956	1	0.1%
4.397178308	3	0.3%
3.267394989	2	0.2%
2.990139703	3	0.3%
2.943643611	4	0.5%

Embarked_Q
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	7.0 KiB

0	809
1	77

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	886
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	809	91.3%
1	77	8.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	809	91.3%
1	77	8.7%

Most occurring characters

Value	Count	Frequency (%)
0	809	91.3%
1	77	8.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	886	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	809	91.3%
1	77	8.7%

Most occurring scripts

Value	Count	Frequency (%)
Common	886	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	809	91.3%
1	77	8.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	886	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	809	91.3%
1	77	8.7%

Embarked_S
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	7.0 KiB

1	644
0	242

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	886
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	644	72.7%
0	242	27.3%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
1	644	72.7%
0	242	27.3%

Most occurring characters

Value	Count	Frequency (%)
1	644	72.7%
0	242	27.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	886	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	644	72.7%
0	242	27.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	886	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	644	72.7%
0	242	27.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	886	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	644	72.7%
0	242	27.3%

Sex_male
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	7.0 KiB

1	575
0	311

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	886
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
1	575	64.9%
0	311	35.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
1	575	64.9%
0	311	35.1%

Most occurring characters

Value	Count	Frequency (%)
1	575	64.9%
0	311	35.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	886	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	575	64.9%
0	311	35.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	886	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	575	64.9%
0	311	35.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	886	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	575	64.9%
0	311	35.1%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	df_index	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare	Embarked_Q	Embarked_S	Sex_male
0	0	1	0	3	-0.561360	1	0	-0.564532	0	1	1
1	1	2	1	1	0.669900	1	0	0.992225	0	0	0
2	2	3	1	3	-0.253545	0	0	-0.548122	0	1	0
3	3	4	1	1	0.439039	1	0	0.550159	0	1	0
4	4	5	0	3	0.439039	0	0	-0.545083	0	1	1
5	5	6	0	3	-0.099638	0	0	-0.535156	1	0	1
6	6	7	0	1	1.901160	0	0	0.520073	0	1	1
7	7	8	0	3	-2.100435	3	1	-0.228423	0	1	1
8	8	9	1	3	-0.176592	0	2	-0.470123	0	1	0
9	9	10	1	2	-1.176990	1	0	-0.009720	0	0	0

Last rows

	df_index	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare	Embarked_Q	Embarked_S	Sex_male
876	881	882	0	3	0.285131	0	0	-0.548832	0	1	1
877	882	883	0	3	-0.561360	0	0	-0.485113	0	1	0
878	883	884	0	2	-0.099638	0	0	-0.485519	0	1	1
879	884	885	0	3	-0.330499	0	0	-0.569394	0	1	1
880	885	886	0	3	0.746854	0	5	-0.032714	1	0	0
881	886	887	0	2	-0.176592	0	0	-0.424740	0	1	1
882	887	888	1	1	-0.792222	0	0	-0.011441	0	1	0
883	888	889	0	3	-0.099638	1	2	-0.170683	0	1	0
884	889	890	1	1	-0.253545	0	0	-0.011441	0	0	1
885	890	891	0	3	0.208177	0	0	-0.552376	1	0	1

Overview

Variables

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows